Real-Time biosignal classification in power-constrained embedded applications is a key step in designing portable e-healtb devices requiring hardware integration along with concurrent signal processing. This paper presents an application based on a novel biomedical System-On-Chip (SoC) for signal acquisition and processing combining a homogeneous multi-core cluster with a versatile bio-potential front-end. The presented implementation acquires raw EMG signals from 3 passive gel-electrodes and classifies 3 hand gestures using a Support Vector Machine (SVM) pattern recognition algorithm. Performance matches state-of-The-Art high-end systems both in terms of recognition accuracy (>S5%) and of real-Time execution (gesture recognition time 300 ms). The power consumption of the employed biomedical SoC is below 10 mW, outperforming implementations on conunercial MCUs by a factor of 10, ensuring a battery life of up to 160 hours with a common Li-ion 1600 mAh battery.
A sub-10mW real-Time implementation for EMG hand gesture recognition based on a multi-core biomedical SoC / Benatti, S.; Rovere, G.; Bosser, J.; Montagna, F.; Farella, E.; Glaser, H.; Schonle, P.; Burger, T.; Fateh, S.; Huang, Q.; Benini, L.. - (2017), pp. 139-144. (Intervento presentato al convegno 7th International Workshop on Advances in Sensors and Interfaces, IWASI 2017 tenutosi a ita nel 2017) [10.1109/IWASI.2017.7974234].
A sub-10mW real-Time implementation for EMG hand gesture recognition based on a multi-core biomedical SoC
Benatti S.;
2017
Abstract
Real-Time biosignal classification in power-constrained embedded applications is a key step in designing portable e-healtb devices requiring hardware integration along with concurrent signal processing. This paper presents an application based on a novel biomedical System-On-Chip (SoC) for signal acquisition and processing combining a homogeneous multi-core cluster with a versatile bio-potential front-end. The presented implementation acquires raw EMG signals from 3 passive gel-electrodes and classifies 3 hand gestures using a Support Vector Machine (SVM) pattern recognition algorithm. Performance matches state-of-The-Art high-end systems both in terms of recognition accuracy (>S5%) and of real-Time execution (gesture recognition time 300 ms). The power consumption of the employed biomedical SoC is below 10 mW, outperforming implementations on conunercial MCUs by a factor of 10, ensuring a battery life of up to 160 hours with a common Li-ion 1600 mAh battery.
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